SeniorFull TimeEN
Cint is a pioneer in research technology (ResTech). Our customers use the Cint platform to post questions and get answers from real people to build business strategies, confidently publish research, accurately measure the impact of digital advertising, and more. The Cint platform is built on a programmatic marketplace, which is the world's largest, with nearly 300 million respondents in over 150 countries who consent to sharing their opinions, motivations, and behaviours.
The Role
We're hiring a Staff MLOps Engineer to own the AI/ML platform at Cint. The immediate focus is supporting the Synthetic Data Platform — models for survey augmentation and respondent profiling — but the role's longer-term remit is broader: Trust Score (our respondent quality and fraud detection model) and other AI/ML initiatives need the same platform capabilities. You'll start by reviewing the current setup and deciding whether to extend it or rebuild parts of it, then build out the shared AI/ML platform from there.
The Team
You'll report into our Infrastructure and Data Engineering organisation, working in close partnership with the AI/ML team in Prague. This is deliberately a platform-with-feature-focus role: your day-to-day delivery serves the Synthetic Data team's needs, but your architectural remit covers all of Cint's AI/ML workloads.
What You'll Do
Assess and decide on the current pipeline: Audit the existing AI/ML training and serving setup. Decide what's worth building on and what needs to be rebuilt. Make the call and own the rationale.
Build the shared AI/ML platform: Training infrastructure, experiment tracking, model registry, serving, monitoring. Built once, used by Synthetic, Trust Score, and whatever comes next.
Oversee the full ML lifecycle: From data ingestion and feature processing to annotation workflows, ensuring the platform facilitates frictionless, rapid model iteration for Data Scientists.
Own training infrastructure on Databricks and Unity Catalog: Make training fast, reproducible, and traceable. Lineage matters; reproducibility matters more.
Model serving: Build the serving layer — low-latency APIs, batch scoring jobs, appropriate caching. Integrate with our Java/Spring services.
Monitoring and drift: Build the observability our models need — data drift, model drift, accuracy regression, business metrics. Grafana dashboards, Prometheus metrics, clear alerts.
Cost and performance: ML compute costs add up. Set the patterns for cost-effective training and serving, representing ML infrastructure spend and ROI credibly to finance stakeholders.
Mentor and multiply: Act as a force multiplier by coaching AI/ML and Infrastructure engineers on engineering best practices. You don’t just "do" the work; you set the bar for what "good" looks like.
Drive AI tooling adoption: Model how AI-native development works for platform teams. Claude Code, agentic workflows, AI-assisted incident response.
Databricks / Spark Native: Comfortable in Databricks. Unity Catalog experience is a strong plus.
Kubernetes & Cloud: You've deployed ML workloads on Kubernetes. AWS (EKS) is our environment; familiarity is a plus.
Who You Are
Deep ML Platform Expertise: You've led ML platform work at a serious scale. You have strong opinions on feature stores, model registries, serving patterns, and what "ML observability" actually means.
Mature Engineering: You’re someone with both a wide and deep background of engineering excellence in a number of disciplines. This is a very senior position in our engineering organisation; setting examples in approach and behaviour is a key trait.
Systems Architect: You think about the platform as a product with real users (your ML team). You design APIs, write docs, and measure adoption.
Technical leader: You lead through standards, RFCs, and credibility — not meetings. You've mentored MLOps engineers into senior ICs.
Pragmatic about buy-vs-build: You know when to adopt a managed service and when to build. You can defend either call to leadership.
Commercially literate: You can justify platform investment to VP / C-suite and translate business priorities into a roadmap.
Working at Cint
Prague-First, Europe-Friendly: Our preferred base is Prague, alongside our existing AI/ML team. Remote work from Germany, Spain or the UK is also possible — these are the markets where we have entities.
AI-Native Engineering: We're rolling out Claude Code and modern agentic tooling across engineering. You'll use it daily — not as a novelty, but as a force multiplier for the complex problems that matter.
High Autonomy: We trust our engineers to make sound decisions and own their work end-to-end.
Our Values
Collaboration is our superpower
Innovation is in our blood
We do what we say
We are caring
More About Cint
We’re proud to be recognised in Newsweek’s 2025 Global Top 100 Most Loved Workplaces®, reflecting our commitment to a culture of trust, respect, and employee growth.
In June 2021, Cint acquired Berlin-based GapFish – the world’s largest ISO certified online panel community in the DACH region – and in January 2022, completed the acquisition of US-based Lucid – a programmatic research technology platform that provides access to first-party survey data in over 110 countries.
Cint Group AB (publ), listed on Nasdaq Stockholm, this growth has made Cint a strong global platform with teams across its many global offices, including Stockholm, London, New York, New Orleans, Singapore, Tokyo and Sydney. (www.cint.com)
Additionally, in a world of AI, we want our candidates to understand our approach to the use of AI during the interview and hiring process, so we'd appreciate you reading our AI usage guide.
